Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations72
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.8 KiB
Average record size in memory154.0 B

Variable types

TimeSeries11
Boolean2
Unsupported1
Numeric3
Categorical3

Timeseries statistics

Number of series11
Time series length72
Starting point2025-04-16 00:00:00
Ending point2025-04-18 23:00:00
Period1 hour
2025-05-15T11:56:25.549872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.615022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

chance_of_snow has constant value "0" Constant
chance_of_rain is highly overall correlated with cloud and 4 other fieldsHigh correlation
cloud is highly overall correlated with chance_of_rain and 4 other fieldsHigh correlation
dewpoint_c is highly overall correlated with chance_of_rain and 3 other fieldsHigh correlation
feelslike_c is highly overall correlated with gust_kph and 7 other fieldsHigh correlation
gust_kph is highly overall correlated with feelslike_c and 5 other fieldsHigh correlation
heatindex_c is highly overall correlated with feelslike_c and 8 other fieldsHigh correlation
humidity is highly overall correlated with cloud and 9 other fieldsHigh correlation
is_day is highly overall correlated with feelslike_c and 3 other fieldsHigh correlation
precip_mm is highly overall correlated with chance_of_rain and 5 other fieldsHigh correlation
pressure_mb is highly overall correlated with chance_of_rain and 2 other fieldsHigh correlation
temp_c is highly overall correlated with feelslike_c and 7 other fieldsHigh correlation
uv is highly overall correlated with feelslike_c and 5 other fieldsHigh correlation
vis_km is highly overall correlated with precip_mmHigh correlation
will_it_rain is highly overall correlated with chance_of_rain and 3 other fieldsHigh correlation
wind_degree is highly overall correlated with wind_dir and 1 other fieldsHigh correlation
wind_dir is highly overall correlated with wind_degreeHigh correlation
wind_kph is highly overall correlated with feelslike_c and 7 other fieldsHigh correlation
windchill_c is highly overall correlated with feelslike_c and 7 other fieldsHigh correlation
vis_km is highly imbalanced (59.1%) Imbalance
wind_kph is non stationary Non stationary
pressure_mb is non stationary Non stationary
humidity is non stationary Non stationary
cloud is non stationary Non stationary
feelslike_c is non stationary Non stationary
heatindex_c is non stationary Non stationary
dewpoint_c is non stationary Non stationary
chance_of_rain is non stationary Non stationary
condition is an unsupported type, check if it needs cleaning or further analysis Unsupported
precip_mm has 51 (70.8%) zeros Zeros
cloud has 1 (1.4%) zeros Zeros
chance_of_rain has 51 (70.8%) zeros Zeros
uv has 36 (50.0%) zeros Zeros

Reproduction

Analysis started2025-05-15 09:56:18.773213
Analysis finished2025-05-15 09:56:25.514296
Duration6.74 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

temp_c
Numeric time series

High correlation 

Distinct63
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.25
Minimum11.1
Maximum25.7
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2025-05-15T11:56:25.682300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.1
5-th percentile11.6
Q113.575
median16.7
Q320.325
95-th percentile24.545
Maximum25.7
Range14.6
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation4.2856787
Coefficient of variation (CV)0.24844514
Kurtosis-0.98056345
Mean17.25
Median Absolute Deviation (MAD)3.45
Skewness0.43170212
Sum1242
Variance18.367042
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.04377701546
2025-05-15T11:56:25.729713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-15T11:56:25.849178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-15T11:56:25.874538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
16.7 3
 
4.2%
12.9 3
 
4.2%
13 2
 
2.8%
14.7 2
 
2.8%
13.6 2
 
2.8%
24.3 2
 
2.8%
11.6 2
 
2.8%
22.2 1
 
1.4%
25.3 1
 
1.4%
25.7 1
 
1.4%
Other values (53) 53
73.6%
ValueCountFrequency (%)
11.1 1
1.4%
11.4 1
1.4%
11.5 1
1.4%
11.6 2
2.8%
11.8 1
1.4%
12 1
1.4%
12.1 1
1.4%
12.2 1
1.4%
12.4 1
1.4%
12.5 1
1.4%
ValueCountFrequency (%)
25.7 1
1.4%
25.3 1
1.4%
25.2 1
1.4%
24.6 1
1.4%
24.5 1
1.4%
24.4 1
1.4%
24.3 2
2.8%
23.9 1
1.4%
23.6 1
1.4%
23.3 1
1.4%
2025-05-15T11:56:25.759059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

is_day
Boolean

High correlation 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size648.0 B
True
42 
False
30 
ValueCountFrequency (%)
True 42
58.3%
False 30
41.7%
2025-05-15T11:56:25.902512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

condition
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size1.1 KiB

wind_kph
Numeric time series

High correlation  Non stationary 

Distinct38
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.716667
Minimum3.2
Maximum22
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2025-05-15T11:56:25.944391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile4.52
Q17.2
median11
Q315.575
95-th percentile20.68
Maximum22
Range18.8
Interquartile range (IQR)8.375

Descriptive statistics

Standard deviation5.2303017
Coefficient of variation (CV)0.44639844
Kurtosis-0.9330755
Mean11.716667
Median Absolute Deviation (MAD)3.8
Skewness0.34506144
Sum843.6
Variance27.356056
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.06075035424
2025-05-15T11:56:25.986437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
2025-05-15T11:56:26.129671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-15T11:56:26.166079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
7.2 6
 
8.3%
7.9 4
 
5.6%
13.3 4
 
5.6%
7.6 3
 
4.2%
13.7 3
 
4.2%
18.4 3
 
4.2%
10.8 3
 
4.2%
6.5 3
 
4.2%
10.4 2
 
2.8%
6.1 2
 
2.8%
Other values (28) 39
54.2%
ValueCountFrequency (%)
3.2 2
 
2.8%
3.6 1
 
1.4%
4.3 1
 
1.4%
4.7 1
 
1.4%
5 2
 
2.8%
6.1 2
 
2.8%
6.5 3
4.2%
6.8 2
 
2.8%
7.2 6
8.3%
7.6 3
4.2%
ValueCountFrequency (%)
22 2
2.8%
21.6 1
 
1.4%
20.9 1
 
1.4%
20.5 1
 
1.4%
20.2 2
2.8%
19.1 2
2.8%
18.7 1
 
1.4%
18.4 3
4.2%
18 1
 
1.4%
16.6 1
 
1.4%
2025-05-15T11:56:26.014701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

wind_degree
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.11111
Minimum90
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-15T11:56:26.220230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile101.1
Q1121
median141
Q3165.25
95-th percentile187.15
Maximum270
Range180
Interquartile range (IQR)44.25

Descriptive statistics

Standard deviation30.960018
Coefficient of variation (CV)0.21633553
Kurtosis2.7587946
Mean143.11111
Median Absolute Deviation (MAD)23
Skewness1.0041196
Sum10304
Variance958.52269
MonotonicityNot monotonic
2025-05-15T11:56:26.427235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139 4
 
5.6%
166 4
 
5.6%
113 3
 
4.2%
143 3
 
4.2%
165 2
 
2.8%
167 2
 
2.8%
141 2
 
2.8%
118 2
 
2.8%
122 2
 
2.8%
147 2
 
2.8%
Other values (42) 46
63.9%
ValueCountFrequency (%)
90 1
1.4%
91 1
1.4%
94 1
1.4%
100 1
1.4%
102 1
1.4%
104 1
1.4%
105 1
1.4%
107 1
1.4%
108 1
1.4%
112 1
1.4%
ValueCountFrequency (%)
270 1
1.4%
209 1
1.4%
207 1
1.4%
191 1
1.4%
184 1
1.4%
178 1
1.4%
176 1
1.4%
174 1
1.4%
172 1
1.4%
170 1
1.4%

wind_dir
Categorical

High correlation 

Distinct7
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
SSE
20 
SE
19 
ESE
18 
S
E
Other values (2)

Length

Max length3
Median length3
Mean length2.4027778
Min length1

Characters and Unicode

Total characters173
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st rowESE
2nd rowESE
3rd rowESE
4th rowSE
5th rowSE

Common Values

ValueCountFrequency (%)
SSE 20
27.8%
SE 19
26.4%
ESE 18
25.0%
S 7
 
9.7%
E 4
 
5.6%
SSW 3
 
4.2%
W 1
 
1.4%

Length

2025-05-15T11:56:26.464670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T11:56:26.491334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sse 20
27.8%
se 19
26.4%
ese 18
25.0%
s 7
 
9.7%
e 4
 
5.6%
ssw 3
 
4.2%
w 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
S 90
52.0%
E 79
45.7%
W 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 173
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 90
52.0%
E 79
45.7%
W 4
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 173
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 90
52.0%
E 79
45.7%
W 4
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 173
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 90
52.0%
E 79
45.7%
W 4
 
2.3%

pressure_mb
Numeric time series

High correlation  Non stationary 

Distinct6
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1014.2222
Minimum1012
Maximum1017
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2025-05-15T11:56:26.533290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1012
5-th percentile1013
Q11013
median1014
Q31015
95-th percentile1016
Maximum1017
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2011992
Coefficient of variation (CV)0.001184355
Kurtosis-0.96181077
Mean1014.2222
Median Absolute Deviation (MAD)1
Skewness0.30854405
Sum73024
Variance1.4428795
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0976633407
2025-05-15T11:56:26.567536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
2025-05-15T11:56:26.673001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-15T11:56:26.701627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1013 23
31.9%
1014 19
26.4%
1015 14
19.4%
1016 13
18.1%
1012 2
 
2.8%
1017 1
 
1.4%
ValueCountFrequency (%)
1012 2
 
2.8%
1013 23
31.9%
1014 19
26.4%
1015 14
19.4%
1016 13
18.1%
1017 1
 
1.4%
ValueCountFrequency (%)
1017 1
 
1.4%
1016 13
18.1%
1015 14
19.4%
1014 19
26.4%
1013 23
31.9%
1012 2
 
2.8%
2025-05-15T11:56:26.589717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

precip_mm
Real number (ℝ)

High correlation  Zeros 

Distinct22
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41069444
Minimum0
Maximum10.37
Zeros51
Zeros (%)70.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-15T11:56:26.736309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0675
95-th percentile2.398
Maximum10.37
Range10.37
Interquartile range (IQR)0.0675

Descriptive statistics

Standard deviation1.4558027
Coefficient of variation (CV)3.5447343
Kurtosis33.047536
Mean0.41069444
Median Absolute Deviation (MAD)0
Skewness5.4192318
Sum29.57
Variance2.1193615
MonotonicityNot monotonic
2025-05-15T11:56:26.765884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 51
70.8%
0.12 1
 
1.4%
0.01 1
 
1.4%
0.43 1
 
1.4%
0.57 1
 
1.4%
0.34 1
 
1.4%
0.24 1
 
1.4%
3.22 1
 
1.4%
0.56 1
 
1.4%
0.54 1
 
1.4%
Other values (12) 12
 
16.7%
ValueCountFrequency (%)
0 51
70.8%
0.01 1
 
1.4%
0.04 1
 
1.4%
0.05 1
 
1.4%
0.12 1
 
1.4%
0.19 1
 
1.4%
0.24 1
 
1.4%
0.29 1
 
1.4%
0.34 1
 
1.4%
0.38 1
 
1.4%
ValueCountFrequency (%)
10.37 1
1.4%
5.49 1
1.4%
3.22 1
1.4%
2.64 1
1.4%
2.2 1
1.4%
0.66 1
1.4%
0.63 1
1.4%
0.6 1
1.4%
0.57 1
1.4%
0.56 1
1.4%

humidity
Numeric time series

High correlation  Non stationary 

Distinct41
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.833333
Minimum31
Maximum96
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2025-05-15T11:56:26.825107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile38.2
Q153.5
median71
Q390
95-th percentile95
Maximum96
Range65
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation20.148044
Coefficient of variation (CV)0.28444297
Kurtosis-1.2342552
Mean70.833333
Median Absolute Deviation (MAD)19
Skewness-0.36677543
Sum5100
Variance405.94366
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.23025918
2025-05-15T11:56:26.875246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
2025-05-15T11:56:26.974789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-15T11:56:27.003695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
91 4
 
5.6%
93 4
 
5.6%
95 4
 
5.6%
90 4
 
5.6%
63 3
 
4.2%
88 3
 
4.2%
96 2
 
2.8%
60 2
 
2.8%
50 2
 
2.8%
71 2
 
2.8%
Other values (31) 42
58.3%
ValueCountFrequency (%)
31 1
1.4%
33 1
1.4%
34 1
1.4%
36 1
1.4%
40 2
2.8%
41 2
2.8%
42 1
1.4%
43 1
1.4%
45 1
1.4%
48 2
2.8%
ValueCountFrequency (%)
96 2
2.8%
95 4
5.6%
94 2
2.8%
93 4
5.6%
91 4
5.6%
90 4
5.6%
89 2
2.8%
88 3
4.2%
87 2
2.8%
86 1
 
1.4%
2025-05-15T11:56:26.902840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

cloud
Numeric time series

High correlation  Non stationary  Zeros 

Distinct41
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.069444
Minimum0
Maximum100
Zeros1
Zeros (%)1.4%
Memory size1.1 KiB
2025-05-15T11:56:27.062804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.55
Q113
median28
Q383.5
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)70.5

Descriptive statistics

Standard deviation36.294308
Coefficient of variation (CV)0.82357081
Kurtosis-1.3851597
Mean44.069444
Median Absolute Deviation (MAD)20.5
Skewness0.52362992
Sum3173
Variance1317.2768
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3378539114
2025-05-15T11:56:27.150779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
2025-05-15T11:56:27.296666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-15T11:56:27.321777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
100 13
 
18.1%
7 4
 
5.6%
18 3
 
4.2%
28 3
 
4.2%
13 3
 
4.2%
8 3
 
4.2%
3 2
 
2.8%
25 2
 
2.8%
23 2
 
2.8%
21 2
 
2.8%
Other values (31) 35
48.6%
ValueCountFrequency (%)
0 1
 
1.4%
2 1
 
1.4%
3 2
2.8%
4 1
 
1.4%
7 4
5.6%
8 3
4.2%
9 2
2.8%
10 2
2.8%
12 1
 
1.4%
13 3
4.2%
ValueCountFrequency (%)
100 13
18.1%
93 1
 
1.4%
91 1
 
1.4%
90 1
 
1.4%
86 1
 
1.4%
85 1
 
1.4%
83 1
 
1.4%
78 1
 
1.4%
77 1
 
1.4%
76 1
 
1.4%
2025-05-15T11:56:27.210632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

feelslike_c
Numeric time series

High correlation  Non stationary 

Distinct60
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.193056
Minimum10.5
Maximum25.9
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2025-05-15T11:56:27.373304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile11.165
Q112.8
median16.7
Q320.325
95-th percentile25.3
Maximum25.9
Range15.4
Interquartile range (IQR)7.525

Descriptive statistics

Standard deviation4.708408
Coefficient of variation (CV)0.27385522
Kurtosis-1.0120968
Mean17.193056
Median Absolute Deviation (MAD)3.85
Skewness0.43662659
Sum1237.9
Variance22.169106
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1204940807
2025-05-15T11:56:27.427342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-15T11:56:27.554608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-15T11:56:27.579204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
14.3 3
 
4.2%
16.7 3
 
4.2%
24.6 3
 
4.2%
12.7 2
 
2.8%
11.8 2
 
2.8%
25.3 2
 
2.8%
11.7 2
 
2.8%
24.7 2
 
2.8%
12.8 2
 
2.8%
19.1 1
 
1.4%
Other values (50) 50
69.4%
ValueCountFrequency (%)
10.5 1
1.4%
10.8 1
1.4%
10.9 1
1.4%
11 1
1.4%
11.3 1
1.4%
11.6 1
1.4%
11.7 2
2.8%
11.8 2
2.8%
12 1
1.4%
12.1 1
1.4%
ValueCountFrequency (%)
25.9 1
 
1.4%
25.7 1
 
1.4%
25.6 1
 
1.4%
25.3 2
2.8%
24.8 1
 
1.4%
24.7 2
2.8%
24.6 3
4.2%
24.2 1
 
1.4%
23.9 1
 
1.4%
21.9 1
 
1.4%
2025-05-15T11:56:27.465678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

windchill_c
Numeric time series

High correlation 

Distinct63
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.059722
Minimum10.5
Maximum25.7
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2025-05-15T11:56:27.631088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile11.165
Q112.8
median16.7
Q320.325
95-th percentile24.545
Maximum25.7
Range15.2
Interquartile range (IQR)7.525

Descriptive statistics

Standard deviation4.5015071
Coefficient of variation (CV)0.26386755
Kurtosis-1.0620568
Mean17.059722
Median Absolute Deviation (MAD)3.85
Skewness0.36518036
Sum1228.3
Variance20.263566
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.02170533272
2025-05-15T11:56:27.680286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-15T11:56:27.814663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-15T11:56:27.839288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
16.7 3
 
4.2%
14.3 3
 
4.2%
11.7 2
 
2.8%
12.8 2
 
2.8%
12.7 2
 
2.8%
24.3 2
 
2.8%
11.8 2
 
2.8%
25.3 1
 
1.4%
22.2 1
 
1.4%
25.7 1
 
1.4%
Other values (53) 53
73.6%
ValueCountFrequency (%)
10.5 1
1.4%
10.8 1
1.4%
10.9 1
1.4%
11 1
1.4%
11.3 1
1.4%
11.6 1
1.4%
11.7 2
2.8%
11.8 2
2.8%
12 1
1.4%
12.1 1
1.4%
ValueCountFrequency (%)
25.7 1
1.4%
25.3 1
1.4%
25.2 1
1.4%
24.6 1
1.4%
24.5 1
1.4%
24.4 1
1.4%
24.3 2
2.8%
23.9 1
1.4%
23.6 1
1.4%
23.3 1
1.4%
2025-05-15T11:56:27.724481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

heatindex_c
Numeric time series

High correlation  Non stationary 

Distinct59
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.55
Minimum11.1
Maximum25.9
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2025-05-15T11:56:27.893587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.1
5-th percentile11.6
Q113.575
median16.7
Q320.325
95-th percentile25.3
Maximum25.9
Range14.8
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation4.6933576
Coefficient of variation (CV)0.26742778
Kurtosis-1.0898449
Mean17.55
Median Absolute Deviation (MAD)3.45
Skewness0.48962194
Sum1263.6
Variance22.027606
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.131844048
2025-05-15T11:56:27.951710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-15T11:56:28.076023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-15T11:56:28.114693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
12.9 3
 
4.2%
24.6 3
 
4.2%
16.7 3
 
4.2%
24.5 2
 
2.8%
11.6 2
 
2.8%
13 2
 
2.8%
14.8 2
 
2.8%
25.3 2
 
2.8%
24.7 2
 
2.8%
13.6 2
 
2.8%
Other values (49) 49
68.1%
ValueCountFrequency (%)
11.1 1
1.4%
11.4 1
1.4%
11.5 1
1.4%
11.6 2
2.8%
11.8 1
1.4%
12 1
1.4%
12.1 1
1.4%
12.2 1
1.4%
12.4 1
1.4%
12.5 1
1.4%
ValueCountFrequency (%)
25.9 1
 
1.4%
25.7 1
 
1.4%
25.6 1
 
1.4%
25.3 2
2.8%
24.8 1
 
1.4%
24.7 2
2.8%
24.6 3
4.2%
24.5 2
2.8%
24.4 1
 
1.4%
24.2 1
 
1.4%
2025-05-15T11:56:27.985033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

dewpoint_c
Numeric time series

High correlation  Non stationary 

Distinct50
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.129167
Minimum5.9
Maximum15.2
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2025-05-15T11:56:28.168645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile7.21
Q110
median11.35
Q312.525
95-th percentile14.69
Maximum15.2
Range9.3
Interquartile range (IQR)2.525

Descriptive statistics

Standard deviation2.1969529
Coefficient of variation (CV)0.19740498
Kurtosis-0.17754324
Mean11.129167
Median Absolute Deviation (MAD)1.3
Skewness-0.31011078
Sum801.3
Variance4.8266021
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.2027768914
2025-05-15T11:56:28.216903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-15T11:56:28.342213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-15T11:56:28.372108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11.8 4
 
5.6%
10.5 4
 
5.6%
11.4 4
 
5.6%
13.1 3
 
4.2%
11.7 3
 
4.2%
10 2
 
2.8%
10.6 2
 
2.8%
11.3 2
 
2.8%
11.5 2
 
2.8%
11.2 2
 
2.8%
Other values (40) 44
61.1%
ValueCountFrequency (%)
5.9 1
1.4%
6 1
1.4%
6.9 1
1.4%
7.1 1
1.4%
7.3 1
1.4%
7.5 1
1.4%
7.6 1
1.4%
8 1
1.4%
8.1 1
1.4%
8.4 1
1.4%
ValueCountFrequency (%)
15.2 1
1.4%
15.1 1
1.4%
15 1
1.4%
14.8 1
1.4%
14.6 1
1.4%
14.5 1
1.4%
14.3 1
1.4%
14.1 1
1.4%
13.8 1
1.4%
13.6 1
1.4%
2025-05-15T11:56:28.263548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

will_it_rain
Boolean

High correlation 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size648.0 B
False
52 
True
20 
ValueCountFrequency (%)
False 52
72.2%
True 20
 
27.8%
2025-05-15T11:56:28.405209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

chance_of_rain
Numeric time series

High correlation  Non stationary  Zeros 

Distinct4
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.416667
Minimum0
Maximum100
Zeros51
Zeros (%)70.8%
Memory size1.1 KiB
2025-05-15T11:56:28.441665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)100

Descriptive statistics

Standard deviation44.805605
Coefficient of variation (CV)1.5767368
Kurtosis-1.0764827
Mean28.416667
Median Absolute Deviation (MAD)0
Skewness0.96290472
Sum2046
Variance2007.5423
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.7155362107
2025-05-15T11:56:28.474399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
2025-05-15T11:56:28.595765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-15T11:56:28.620620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 51
70.8%
100 19
 
26.4%
76 1
 
1.4%
70 1
 
1.4%
ValueCountFrequency (%)
0 51
70.8%
70 1
 
1.4%
76 1
 
1.4%
100 19
 
26.4%
ValueCountFrequency (%)
100 19
 
26.4%
76 1
 
1.4%
70 1
 
1.4%
0 51
70.8%
2025-05-15T11:56:28.513529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

chance_of_snow
Categorical

Constant 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
72 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72
100.0%

Length

2025-05-15T11:56:28.682582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T11:56:28.702015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 72
100.0%

Most occurring characters

ValueCountFrequency (%)
0 72
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72
100.0%

vis_km
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
10.0
59 
2.0
7.0
 
3
5.0
 
1
9.0
 
1

Length

Max length4
Median length4
Mean length3.8194444
Min length3

Characters and Unicode

Total characters275
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.8%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row10.0

Common Values

ValueCountFrequency (%)
10.0 59
81.9%
2.0 8
 
11.1%
7.0 3
 
4.2%
5.0 1
 
1.4%
9.0 1
 
1.4%

Length

2025-05-15T11:56:28.723676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-15T11:56:28.746970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
10.0 59
81.9%
2.0 8
 
11.1%
7.0 3
 
4.2%
5.0 1
 
1.4%
9.0 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 131
47.6%
. 72
26.2%
1 59
21.5%
2 8
 
2.9%
7 3
 
1.1%
5 1
 
0.4%
9 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 275
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 131
47.6%
. 72
26.2%
1 59
21.5%
2 8
 
2.9%
7 3
 
1.1%
5 1
 
0.4%
9 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 275
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 131
47.6%
. 72
26.2%
1 59
21.5%
2 8
 
2.9%
7 3
 
1.1%
5 1
 
0.4%
9 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 275
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 131
47.6%
. 72
26.2%
1 59
21.5%
2 8
 
2.9%
7 3
 
1.1%
5 1
 
0.4%
9 1
 
0.4%

gust_kph
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.755556
Minimum4.6
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-15T11:56:28.789521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile5.855
Q111.825
median15.3
Q319.8
95-th percentile25.145
Maximum27.7
Range23.1
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation5.6131106
Coefficient of variation (CV)0.35626231
Kurtosis-0.64348539
Mean15.755556
Median Absolute Deviation (MAD)4.05
Skewness-0.0095945247
Sum1134.4
Variance31.507011
MonotonicityNot monotonic
2025-05-15T11:56:28.826983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.1 4
 
5.6%
22 3
 
4.2%
13.9 2
 
2.8%
17.1 2
 
2.8%
15.3 2
 
2.8%
14.7 2
 
2.8%
10.3 2
 
2.8%
11.3 2
 
2.8%
12 2
 
2.8%
23.2 2
 
2.8%
Other values (47) 49
68.1%
ValueCountFrequency (%)
4.6 1
1.4%
5 1
1.4%
5.5 1
1.4%
5.8 1
1.4%
5.9 1
1.4%
6.5 1
1.4%
8 1
1.4%
8.4 1
1.4%
9.4 1
1.4%
9.6 1
1.4%
ValueCountFrequency (%)
27.7 1
 
1.4%
25.3 2
2.8%
25.2 1
 
1.4%
25.1 1
 
1.4%
24.8 1
 
1.4%
23.6 1
 
1.4%
23.2 2
2.8%
22 3
4.2%
21.1 4
5.6%
20.7 1
 
1.4%

uv
Numeric time series

High correlation  Zeros 

Distinct25
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95694444
Minimum0
Maximum5.4
Zeros36
Zeros (%)50.0%
Memory size1.1 KiB
2025-05-15T11:56:28.870887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.05
Q30.875
95-th percentile4.89
Maximum5.4
Range5.4
Interquartile range (IQR)0.875

Descriptive statistics

Standard deviation1.6481168
Coefficient of variation (CV)1.7222701
Kurtosis1.4711493
Mean0.95694444
Median Absolute Deviation (MAD)0.05
Skewness1.7057401
Sum68.9
Variance2.7162891
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.006935853596
2025-05-15T11:56:28.908963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
2025-05-15T11:56:29.042888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-15T11:56:29.068419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 36
50.0%
0.3 4
 
5.6%
0.4 4
 
5.6%
0.1 3
 
4.2%
0.2 3
 
4.2%
4.8 2
 
2.8%
1.1 2
 
2.8%
1.8 1
 
1.4%
4.2 1
 
1.4%
0.5 1
 
1.4%
Other values (15) 15
20.8%
ValueCountFrequency (%)
0 36
50.0%
0.1 3
 
4.2%
0.2 3
 
4.2%
0.3 4
 
5.6%
0.4 4
 
5.6%
0.5 1
 
1.4%
0.6 1
 
1.4%
0.7 1
 
1.4%
0.8 1
 
1.4%
1.1 2
 
2.8%
ValueCountFrequency (%)
5.4 1
1.4%
5.3 1
1.4%
5.2 1
1.4%
5 1
1.4%
4.8 2
2.8%
4.5 1
1.4%
4.2 1
1.4%
3.7 1
1.4%
3.5 1
1.4%
3.1 1
1.4%
2025-05-15T11:56:28.934130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Interactions

2025-05-15T11:56:25.037754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.091929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.608162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.042398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.461916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.860367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.708645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.099989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.470719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.989848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.396043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.794850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.160752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.672273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.065572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.145105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.638564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.071252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.491152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.888995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.736939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.126459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.500536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.018151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.425372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.822014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.189796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.699107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.093589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.208463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.669978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.102490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.521341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.917895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.767554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.155436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.532935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.049765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.455485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.849532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.218161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.726961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.119519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.250630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.701264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.131722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.550793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.949958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.795912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.183953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.562154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.079789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.485954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.879155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.381230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.754162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.145494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.286248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.735918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.161507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.577809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.976572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.825030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.211270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.591721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.109671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.515148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.904614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.407500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.779536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.170670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.332304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.766239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.189812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.603243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.002864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.852628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.235865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.619170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.138628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.543678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.928496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.433673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.804020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.196343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.376127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.798089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.219117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.631130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.029040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.880121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.261606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.647630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.168265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.571158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.954632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.460479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.829900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.221609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.403871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.827874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.248708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.659269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.056702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.906240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.286849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.676026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.196442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.599291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.980555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.485760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.854577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.249969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.437250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.860552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.284109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.691256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.085821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.936317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.314443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.816208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.225824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.628603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.009244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.513036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.883653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.277882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.468662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.892315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.315361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.721170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.546246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.966474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.341825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.847028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.255886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.659733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.035757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.542203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.912614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.304526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.499791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.923563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.346571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.749564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.575662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.994741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.369457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.876428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.285178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.686658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.062603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.570282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.939714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.329231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.527421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.952690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.374367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.775756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.627569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.020890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.394999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.903231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.312995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.713441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.086657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.595108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.963824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.355151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.554791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.983537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.404489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.803786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.657559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.047768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.420521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.933611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.341180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.741262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.112742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.621110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.990169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.379706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:19.580719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.012812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.434032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:20.831141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:21.682305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.073711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.445064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:22.961611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.368864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:23.768459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.136748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:24.646090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-15T11:56:25.012973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-15T11:56:29.125094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
chance_of_rainclouddewpoint_cfeelslike_cgust_kphheatindex_chumidityis_dayprecip_mmpressure_mbtemp_cuvvis_kmwill_it_rainwind_degreewind_dirwind_kphwindchill_c
chance_of_rain1.0000.7670.613-0.249-0.149-0.2130.4970.0000.986-0.539-0.218-0.1590.1830.9860.3080.261-0.013-0.254
cloud0.7671.0000.549-0.349-0.290-0.3280.5420.0000.766-0.435-0.345-0.3500.0000.8630.0780.171-0.208-0.361
dewpoint_c0.6130.5491.0000.035-0.0630.0600.4260.3600.616-0.3660.033-0.3060.2930.6070.1710.111-0.0520.019
feelslike_c-0.249-0.3490.0351.0000.5810.995-0.8600.590-0.2610.3420.9940.5520.2350.2620.4100.0000.6190.999
gust_kph-0.149-0.290-0.0630.5811.0000.607-0.5360.223-0.1620.3370.6110.4690.0000.3000.4750.3220.9490.579
heatindex_c-0.213-0.3280.0600.9950.6071.000-0.8540.711-0.2260.3320.9980.5630.1890.5070.4410.1830.6480.993
humidity0.4970.5420.426-0.860-0.536-0.8541.0000.4070.521-0.503-0.861-0.6560.0000.457-0.2830.095-0.571-0.864
is_day0.0000.0000.3600.5900.2230.7110.4071.0000.1120.0000.7180.4120.1790.0000.2240.2770.3330.592
precip_mm0.9860.7660.616-0.261-0.162-0.2260.5210.1121.000-0.554-0.230-0.1760.7470.3740.2840.000-0.025-0.264
pressure_mb-0.539-0.435-0.3660.3420.3370.332-0.5030.000-0.5541.0000.3360.0950.0880.498-0.0090.0000.2870.342
temp_c-0.218-0.3450.0330.9940.6110.998-0.8610.718-0.2300.3361.0000.5710.1850.4830.4440.1690.6540.996
uv-0.159-0.350-0.3060.5520.4690.563-0.6560.412-0.1760.0950.5711.0000.0000.0000.3920.0000.6250.555
vis_km0.1830.0000.2930.2350.0000.1890.0000.1790.7470.0880.1850.0001.0000.4340.0000.0000.0000.235
will_it_rain0.9860.8630.6070.2620.3000.5070.4570.0000.3740.4980.4830.0000.4341.0000.3780.2590.4390.282
wind_degree0.3080.0780.1710.4100.4750.441-0.2830.2240.284-0.0090.4440.3920.0000.3781.0000.8040.5270.413
wind_dir0.2610.1710.1110.0000.3220.1830.0950.2770.0000.0000.1690.0000.0000.2590.8041.0000.3880.075
wind_kph-0.013-0.208-0.0520.6190.9490.648-0.5710.333-0.0250.2870.6540.6250.0000.4390.5270.3881.0000.618
windchill_c-0.254-0.3610.0190.9990.5790.993-0.8640.592-0.2640.3420.9960.5550.2350.2820.4130.0750.6181.000

Missing values

2025-05-15T11:56:25.430516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-15T11:56:25.482370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

temp_cis_dayconditionwind_kphwind_degreewind_dirpressure_mbprecip_mmhumiditycloudfeelslike_cwindchill_cheatindex_cdewpoint_cwill_it_rainchance_of_rainchance_of_snowvis_kmgust_kphuv
datetime
2025-04-16 00:00:0012.4False{'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/night/143.png', 'code': 1030}7.2116ESE1013.00.0961812.012.012.411.8False002.013.90.0
2025-04-16 01:00:0012.2False{'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/night/143.png', 'code': 1030}6.5118ESE1013.00.0962511.811.812.211.5False002.012.50.0
2025-04-16 02:00:0012.0False{'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/night/143.png', 'code': 1030}7.9123ESE1014.00.0951611.311.312.011.2False002.015.10.0
2025-04-16 03:00:0011.6False{'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/night/143.png', 'code': 1030}7.6127SE1014.00.0952811.011.011.610.8False002.014.70.0
2025-04-16 04:00:0011.4False{'text': 'Clear ', 'icon': '//cdn.weatherapi.com/weather/64x64/night/113.png', 'code': 1000}7.2124SE1014.00.0941810.810.811.410.5False0010.013.90.0
2025-04-16 05:00:0011.1False{'text': 'Clear ', 'icon': '//cdn.weatherapi.com/weather/64x64/night/113.png', 'code': 1000}7.2118ESE1014.00.0931310.510.511.110.0False0010.014.00.0
2025-04-16 06:00:0011.5True{'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}7.2108ESE1015.00.0881310.910.911.59.7False0010.012.80.0
2025-04-16 07:00:0013.0True{'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}7.9121ESE1015.00.0771212.612.613.09.2False0010.011.30.2
2025-04-16 08:00:0014.8True{'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}10.1133SE1015.00.067914.314.314.88.8False0010.012.00.8
2025-04-16 09:00:0016.7True{'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}11.2143SE1015.00.058716.716.716.78.4False0010.012.91.8
temp_cis_dayconditionwind_kphwind_degreewind_dirpressure_mbprecip_mmhumiditycloudfeelslike_cwindchill_cheatindex_cdewpoint_cwill_it_rainchance_of_rainchance_of_snowvis_kmgust_kphuv
datetime
2025-04-18 14:00:0012.9True{'text': 'Light drizzle', 'icon': '//cdn.weatherapi.com/weather/64x64/day/266.png', 'code': 1153}13.3167SSE1013.00.43898511.611.612.911.1True10002.017.10.3
2025-04-18 15:00:0013.0True{'text': 'Cloudy ', 'icon': '//cdn.weatherapi.com/weather/64x64/day/119.png', 'code': 1006}6.1166SSE1013.00.00867812.812.813.010.6False0010.08.00.3
2025-04-18 16:00:0013.6True{'text': 'Partly Cloudy ', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}4.7131SE1013.00.00823913.913.913.610.5False0010.05.90.2
2025-04-18 17:00:0014.5True{'text': 'Patchy rain nearby', 'icon': '//cdn.weatherapi.com/weather/64x64/day/176.png', 'code': 1063}7.6112ESE1013.00.01777614.314.314.510.5True76010.09.40.6
2025-04-18 18:00:0013.8True{'text': 'Patchy rain nearby', 'icon': '//cdn.weatherapi.com/weather/64x64/day/176.png', 'code': 1063}6.890E1013.00.04815013.713.713.810.7False70010.010.30.1
2025-04-18 19:00:0012.9True{'text': 'Partly Cloudy ', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}6.5102ESE1013.00.00852712.712.712.910.5False0010.011.30.0
2025-04-18 20:00:0012.5False{'text': 'Clear ', 'icon': '//cdn.weatherapi.com/weather/64x64/night/113.png', 'code': 1000}6.5113ESE1014.00.00872112.312.312.510.4False0010.011.10.0
2025-04-18 21:00:0012.1False{'text': 'Partly Cloudy ', 'icon': '//cdn.weatherapi.com/weather/64x64/night/116.png', 'code': 1003}6.191E1014.00.00893111.811.812.110.3False0010.010.30.0
2025-04-18 22:00:0011.8False{'text': 'Partly Cloudy ', 'icon': '//cdn.weatherapi.com/weather/64x64/night/116.png', 'code': 1003}5.0104ESE1014.00.00914211.711.711.810.3False0010.08.40.0
2025-04-18 23:00:0011.6False{'text': 'Partly Cloudy ', 'icon': '//cdn.weatherapi.com/weather/64x64/night/116.png', 'code': 1003}3.2122ESE1014.00.00914212.212.211.610.2False0010.05.50.0